A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain
The accurate identification of traffic loads acting on bridges provides an effective basis for the traffic control and operation of in-service bridges. To improve the efficiency and accuracy of loading identification, we propose an efficient multiparameter identification method with a Legendre neura...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7785 |
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| author | He Zhang Ruihong Shen Yuhui Zhou Cun Zhang Zhicheng Zhang |
| author_facet | He Zhang Ruihong Shen Yuhui Zhou Cun Zhang Zhicheng Zhang |
| author_sort | He Zhang |
| collection | DOAJ |
| description | The accurate identification of traffic loads acting on bridges provides an effective basis for the traffic control and operation of in-service bridges. To improve the efficiency and accuracy of loading identification, we propose an efficient multiparameter identification method with a Legendre neural network (LNN) for the monitoring of traffic loads across the full spatiotemporal domain. Compared to conventional studies that suffer from ill-posed problems and neural network-based means that lack a physically interpretable model, with the proposed strategy, both the explicit expression and time histories of the traffic load can be simultaneously obtained. Meanwhile, inaccurate load identification at the bridge’s supports, which is caused by ill-posed problems, does not exist in the identification process using the LNN. After the training and optimization of the LNN, its identification accuracy for speed and the magnitude of forces reached 98.6% and 98.3%, respectively. The results suggest that an identification method with a well-trained LNN is insensitive to noise. |
| format | Article |
| id | doaj-art-328fc710020e4bd4a8794b9d45cce50d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-328fc710020e4bd4a8794b9d45cce50d2024-12-13T16:32:46ZengMDPI AGSensors1424-82202024-12-012423778510.3390/s24237785A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal DomainHe Zhang0Ruihong Shen1Yuhui Zhou2Cun Zhang3Zhicheng Zhang4College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaCollege of Hydraulic and Civil Engineering, Tibet Agriculture and Animal Husbandry University, Linzhi 860000, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaThe accurate identification of traffic loads acting on bridges provides an effective basis for the traffic control and operation of in-service bridges. To improve the efficiency and accuracy of loading identification, we propose an efficient multiparameter identification method with a Legendre neural network (LNN) for the monitoring of traffic loads across the full spatiotemporal domain. Compared to conventional studies that suffer from ill-posed problems and neural network-based means that lack a physically interpretable model, with the proposed strategy, both the explicit expression and time histories of the traffic load can be simultaneously obtained. Meanwhile, inaccurate load identification at the bridge’s supports, which is caused by ill-posed problems, does not exist in the identification process using the LNN. After the training and optimization of the LNN, its identification accuracy for speed and the magnitude of forces reached 98.6% and 98.3%, respectively. The results suggest that an identification method with a well-trained LNN is insensitive to noise.https://www.mdpi.com/1424-8220/24/23/7785traffic load identificationLegendre neural networknetwork optimizationartificial intelligence |
| spellingShingle | He Zhang Ruihong Shen Yuhui Zhou Cun Zhang Zhicheng Zhang A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain Sensors traffic load identification Legendre neural network network optimization artificial intelligence |
| title | A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain |
| title_full | A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain |
| title_fullStr | A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain |
| title_full_unstemmed | A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain |
| title_short | A Legendre Neural Network-Based Approach to Multiparameter Identification of Traffic Loads Across the Full Spatiotemporal Domain |
| title_sort | legendre neural network based approach to multiparameter identification of traffic loads across the full spatiotemporal domain |
| topic | traffic load identification Legendre neural network network optimization artificial intelligence |
| url | https://www.mdpi.com/1424-8220/24/23/7785 |
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